Of course. This is a very common question, especially when dealing with legacy systems or older codebases. Here’s a comprehensive guide to using SciPy with Python 2.7.

The Short Answer

Yes, SciPy can be installed for Python 2.7. The last version of SciPy to officially support Python 2.7 was scipy==1.2.0.

Therefore, you must install this specific version.

If you try to install a newer version of SciPy (e.g., 3.0 or later), it will fail because those versions dropped support for Python 2.7.

Step-by-Step Installation Guide

The best and most reliable way to install a specific version of SciPy for Python 2.7 is by using a virtual environment. This prevents conflicts with any Python 3 installations you might have.

Prerequisites

1. Python 2.7 Installed: Make sure you have Python 2.7 on your system and that it's accessible via the command line. You can check by running:

   python --version

   It should output Python 2.7.x.

2. pip for Python 2.7: You need pip to install packages. If you don't have it, you can install it for Python 2.7 like this:

   # On Linux/macOS
   python -m ensurepip --upgrade
   # On Windows
   python -m ensurepip --upgrade

Recommended Method: Using a Virtual Environment

This is the cleanest approach.

1. Create a Virtual Environment: This creates an isolated directory for your Python 2.7 project.

   # Create the environment (e.g., named 'py27-env')
   python -m virtualenv py27-env

2. Activate the Virtual Environment: You must activate it in every new terminal session where you want to work on this project.

   • On Linux or macOS:

     source py27-env/bin/activate

   • On Windows (Command Prompt):

     py27_env\Scripts\activate.bat

   • On Windows (PowerShell):

     py27_env\Scripts\Activate.ps1

     After activation, your command prompt will be prefixed with (py27-env).

3. Install SciPy 1.2.0: Now that your virtual environment is active and using Python 2.7, you can safely install the package.

   # The '==' is crucial to specify the exact version
   pip install scipy==1.2.0

4. Verify the Installation: Run a quick test in a Python interpreter to make sure everything is working.

   # Make sure your virtual environment is still active
   (py27-env) $ python
   >>> import scipy
   >>> print scipy.__version__
   1.2.0
   >>> import numpy
   >>> print numpy.__version__
   # It will likely install a compatible numpy version, e.g., 1.16.6
   >>> exit()

Critical Considerations and Warnings

NumPy Dependency

SciPy depends on NumPy. When you install scipy==1.2.0, pip will automatically install a compatible version of NumPy for you (in this case, it will be numpy==1.16.6 or similar). You don't need to install NumPy separately.

SciPy EOL and Security Vulnerabilities

This is the most important warning: Python 2.7 and its associated packages like SciPy 1.2.0 are End-of-Life (EOL).

• No Security Updates: You will not receive any security patches. Using this setup in a production environment is extremely risky.
• Bugs are Not Fixed: Any bugs discovered in SciPy since 2025 will not be fixed in the version you are using.
• Modern Libraries Won't Work: Almost all modern data science libraries (like pandas, scikit-learn, tensorflow, pytorch) have dropped support for Python 2.7 and require Python 3. You will not be able to use them in this environment.

Performance

Older versions of NumPy and SciPy (pre-1.17) are significantly slower than modern versions. The performance gains from improvements like the BLAS/LAPACK integration and general code optimization over the last 5+ years are substantial.

Why Are You Still Using Python 2.7?

It's helpful to understand the reason, as it might point to a better solution.

• Legacy Codebase? If you are maintaining old software, the best long-term strategy is to migrate the code to Python 3. Python 2.7 support was officially over in 2025. The cost of maintaining a Python 2.7 environment will only increase over time.
• Old Hardware/OS? If you are on a very old system that cannot run a modern Python 3, you might be stuck. In that case, using a virtual environment as described above is your best bet for isolating the old dependencies.
• Specific Legacy Library? If a single, critical library is the reason, see if there is a modern alternative. The Python ecosystem has evolved significantly.

Summary Table

Final Recommendation: While it's technically possible to use SciPy with Python 2.7, it is strongly advised to plan a migration to Python 3. The Python 3 ecosystem is modern, secure, and where all active development is happening.